Decision Boundary
The boundary that separates different classes in a machine learning model, determining how new data points are classified.
What is a Decision Boundary?
During classification, a model learns patterns that distinguish one class from another. The decision boundary represents where the model’s predicted classification changes between those classes. For simple datasets, it may appear as a line or curve, while models working with many features can create more complex boundaries across multiple dimensions.
Why is a Decision Boundary Important?
Decision boundaries help explain how classification models separate different categories of data. Examining them can reveal whether a model has learned meaningful patterns, is overfitting its training data, or struggles to distinguish between overlapping classes. They can also help developers compare the behavior of different classification algorithms.
Common use cases
Decision boundaries are commonly used in binary classification, multi-class classification, model visualization, pattern recognition, support vector machines, and neural networks.